Abstract: Stem cell research is pivotal in advancing regenerative medicine and biological studies. However, accurately identifying stem cells amidst a heterogeneous population of cells remains a significant challenge, often requiring labor-intensive manual processor advanced equipment. This project proposes an AI-powered device for accurate stem cell detection, combining deep learning techniques with advanced image analysis to automate and enhance the identification process. The device employs a convolutional neural network (CNN) trained on labeled microscopic images of stem cells, enabling precise classification based on unique cellular features. The model is integrated into a user-friendly software system, capable of analyzing static images or real-time video feeds, providing instant and reliable results.
Key innovations include robust data pre-processing using augmentation techniques to improve model generalization, real-time detection capabilities, and adaptability for diverse imaging setups. By leveraging the power of AI, this solution reduces the need for extensive manual effort, minimizes error rates, and accelerates the work flow in laboratory and clinical settings. This project demonstrates the potential of artificial intelligence in biomedical applications, aiming to democratize access to efficient diagnostic tools and stream line stem cell research processes. Future enhancements include hardware integration for portable use and application expansion to other cell types and biomedical imaging challenges
Keywords: convolutional neural network, diverse image, clinical setting.
|
DOI:
10.17148/IJARCCE.2025.14717
